# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/distributed/parallel_state.py

# Copyright 2023 The vLLM team.
# Adapted from
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Distributed state.
It takes over the control of the distributed environment from PyTorch.
The typical workflow is:

- call `init_distributed_environment` to initialize the distributed environment.
- call `initialize_model_parallel` or `ensure_model_parallel_initialized` to
 initialize the model parallel groups.

- any code dealing with the distributed stuff

- call `destroy_model_parallel` to destroy the model parallel groups.
- call `destroy_distributed_environment` to destroy the distributed environment.

If you only need to use the distributed environment without model/pipeline
 parallelism, you can skip the model parallel initialization and destruction
 steps.
"""

import contextlib
import gc
import logging
import os
import pickle
import weakref
from collections import namedtuple
from contextlib import contextmanager, nullcontext
from dataclasses import dataclass
from datetime import timedelta
from multiprocessing import shared_memory
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest.mock import patch

import torch
import torch.distributed
from torch.distributed import Backend, ProcessGroup

from sglang.srt import platforms
from sglang.srt.compilation.compilation_config import register_split_op
from sglang.srt.distributed.utils import set_global_tcp_store
from sglang.srt.environ import envs
from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import (
    is_in_tc_piecewise_cuda_graph,
)
from sglang.srt.platforms.device_mixin import _DEVICE_TO_DISTRIBUTED_BACKEND
from sglang.srt.runtime_context import (
    get_global_dwdp_manager,
    set_global_dwdp_manager,
)
from sglang.srt.utils import (
    get_current_device_stream_fast,
    get_int_env_var,
    is_cpu,
    is_cuda,
    is_cuda_alike,
    is_gfx95_supported,
    is_hip,
    is_musa,
    is_npu,
    is_shm_available,
    is_xpu,
)
from sglang.srt.utils.custom_op import register_custom_op
from sglang.srt.utils.network import get_local_ip_auto
from sglang.srt.utils.stale_shm_cleanup import make_shm_name

_is_npu = is_npu()
_is_cpu = is_cpu()
_is_xpu = is_xpu()
_is_musa = is_musa()

TensorMetadata = namedtuple("TensorMetadata", ["device", "dtype", "size"])

# use int value instead of ReduceOp.SUM to support torch compile
REDUCE_OP_SUM = int(torch.distributed.ReduceOp.SUM)

# Reuse the user-provided distributed timeout for model-parallel subgroup
# creation so runtime collectives do not silently fall back to backend defaults.
_MODEL_PARALLEL_GROUP_TIMEOUT: Optional[timedelta] = None


def get_torch_distributed_pg_options(group_name=None):
    if not _is_npu:
        return None

    # Only create HCCL options for default group or MoE-related groups
    if group_name is not None and "moe" not in group_name:
        return None

    import torch_npu

    options = torch_npu._C._distributed_c10d.ProcessGroupHCCL.Options()
    hccl_buffer_size = int(
        os.environ.get("DEEPEP_HCCL_BUFFSIZE") or os.environ.get("HCCL_BUFFSIZE") or 200
    )
    options.hccl_config = {"hccl_buffer_size": hccl_buffer_size}
    return options


@dataclass
class GraphCaptureContext:
    stream: torch.get_device_module().Stream


@dataclass
class P2PWork:
    work: Optional[torch.distributed.Work]
    payload: Optional[torch.Tensor]


def _split_tensor_dict(
    tensor_dict: Dict[str, Union[torch.Tensor, Any]],
) -> Tuple[List[Tuple[str, Any]], List[torch.Tensor]]:
    """Split the tensor dictionary into two parts:
    1. A list of (key, value) pairs. If the value is a tensor, it is replaced
         by its metadata.
    2. A list of tensors.
    """
    metadata_list: List[Tuple[str, Any]] = []
    tensor_list: List[torch.Tensor] = []
    for key, value in tensor_dict.items():
        if isinstance(value, torch.Tensor):
            # Note: we cannot use `value.device` here,
            # because it contains not only the device type but also the device
            # index (e.g. "cuda:0"). We only need the device type.
            # receiving side will set the device index.
            device = value.device.type
            metadata_list.append(
                (key, TensorMetadata(device, value.dtype, value.size()))
            )
            tensor_list.append(value)
        else:
            metadata_list.append((key, value))
    return metadata_list, tensor_list


_group_name_counter: Dict[str, int] = {}


def _get_unique_name(name: str) -> str:
    """Get a unique name for the group.
    Example:
    _get_unique_name("tp") -> "tp:0"
    _get_unique_name("tp") -> "tp:1"
    """
    if name not in _group_name_counter:
        _group_name_counter[name] = 0
    newname = f"{name}:{_group_name_counter[name]}"
    _group_name_counter[name] += 1
    return newname


_groups: Dict[str, Callable[[], Optional["GroupCoordinator"]]] = {}


def _register_group(group: "GroupCoordinator") -> None:
    _groups[group.unique_name] = weakref.ref(group)


@register_custom_op(mutates_args=["tensor"])
@register_split_op()
def inplace_all_reduce(tensor: torch.Tensor, group_name: str) -> None:
    assert group_name in _groups, f"Group {group_name} is not found."
    group = _groups[group_name]()
    if group is None:
        raise ValueError(f"Group {group_name} is destroyed.")
    group._all_reduce_in_place(tensor)


@register_custom_op(out_shape="tensor")
def outplace_all_reduce(
    tensor: torch.Tensor, group_name: str, outplace_all_reduce_method: str
) -> torch.Tensor:
    assert group_name in _groups, f"Group {group_name} is not found."
    group = _groups[group_name]()
    if group is None:
        raise ValueError(f"Group {group_name} is destroyed.")
    return group._all_reduce_out_place(tensor, outplace_all_reduce_method)


@register_custom_op(mutates_args=["output"])
def reg_all_gather_into_tensor(
    output: torch.Tensor, input: torch.Tensor, group_name: str
) -> None:
    assert group_name in _groups, f"Group {group_name} is not found."
    group = _groups[group_name]()
    if group is None:
        raise ValueError(f"Group {group_name} is destroyed.")
    group._all_gather_into_tensor(output, input)


@register_custom_op(mutates_args=["output"])
def reg_reduce_scatter_tensor(
    output: torch.Tensor, input: torch.Tensor, group_name: str
) -> None:
    assert group_name in _groups, f"Group {group_name} is not found."
    group = _groups[group_name]()
    if group is None:
        raise ValueError(f"Group {group_name} is destroyed.")
    group._reduce_scatter_tensor(output, input)


@register_custom_op(mutates_args=["output"])
def reg_all_to_all_single(
    output: torch.Tensor, input: torch.Tensor, group_name: str
) -> None:
    assert group_name in _groups, f"Group {group_name} is not found."
    group = _groups[group_name]()
    if group is None:
        raise ValueError(f"Group {group_name} is destroyed.")
    group._all_to_all_single(output, input)


class GroupCoordinator:
    """
    PyTorch ProcessGroup wrapper for a group of processes.
    PyTorch ProcessGroup is bound to one specific communication backend,
        e.g. NCCL, Gloo, MPI, etc.
    GroupCoordinator takes charge of all the communication operations among
        the processes in the group. It can route the communication to
        a specific implementation (e.g. switch allreduce implementation
        based on the tensor size and cuda graph mode).
    """

    # available attributes:
    rank: int  # global rank
    ranks: List[int]  # global ranks in the group
    world_size: int  # size of the group
    # difference between `local_rank` and `rank_in_group`:
    # if we have a group of size 4 across two nodes:
    # Process | Node | Rank | Local Rank | Rank in Group
    #   0     |   0  |  0   |     0      |       0
    #   1     |   0  |  1   |     1      |       1
    #   2     |   1  |  2   |     0      |       2
    #   3     |   1  |  3   |     1      |       3
    local_rank: int  # local rank used to assign devices
    rank_in_group: int  # rank inside the group
    cpu_group: ProcessGroup  # group for CPU communication
    device_group: ProcessGroup  # group for device communication
    use_pynccl: bool  # a hint of whether to use PyNccl
    use_pymscclpp: bool  # a hint of whether to use PyMsccl
    use_custom_allreduce: bool  # a hint of whether to use CustomAllreduce
    use_torch_symm_mem_all_reduce: (
        bool  # a hint of whether to use TorchSymmMemAllReduce
    )
    use_message_queue_broadcaster: (
        bool  # a hint of whether to use message queue broadcaster
    )
    # communicators are only created for world size > 1
    pynccl_comm: Optional[Any]  # PyNccl communicator
    ca_comm: Optional[Any]  # Custom allreduce communicator
    torch_symm_mem_comm: Optional[Any]  # Torch symm mem communicator
    mq_broadcaster: Optional[Any]  # shared memory broadcaster

    def __init__(
        self,
        group_ranks: List[List[int]],
        local_rank: int,
        torch_distributed_backend: Union[str, Backend],
        use_pynccl: bool,
        use_pymscclpp: bool,
        use_custom_allreduce: bool,
        use_torch_symm_mem_all_reduce: bool,
        use_hpu_communicator: bool,
        use_xpu_communicator: bool,
        use_npu_communicator: bool,
        use_message_queue_broadcaster: bool = False,
        group_name: Optional[str] = None,
        gloo_timeout: timedelta = timedelta(seconds=120 * 60),
        recovered_rank: bool = False,
        rank_offset: int = 0,
        max_world_size: Optional[int] = None,
    ):
        # Set group info
        group_name = group_name or "anonymous"
        self.unique_name = _get_unique_name(group_name)
        _register_group(self)

        # Set rank info
        self.rank = torch.distributed.get_rank()
        # Joiner group ranks are local; shift them into global rank space.
        if rank_offset > 0:
            group_ranks = [[r + rank_offset for r in ranks] for ranks in group_ranks]
        self.local_rank = local_rank
        self.device_group = None
        self.cpu_group = None
        self.local_size = get_int_env_var("LOCAL_SIZE", 0)

        if is_cuda_alike():
            device_id = (
                0 if envs.SGLANG_ONE_VISIBLE_DEVICE_PER_PROCESS.get() else local_rank
            )
            self.device = torch.device(f"cuda:{device_id}")
        elif _is_npu:
            self.device = torch.device(f"npu:{local_rank}")
        elif _is_xpu:
            self.device = torch.device(f"xpu:{local_rank}")
        elif _is_musa:
            self.device = torch.device(f"musa:{local_rank}")
        else:
            self.device = torch.device("cpu")
        self.device_module = torch.get_device_module(self.device)

        for ranks in group_ranks:
            subgroup_timeout = _MODEL_PARALLEL_GROUP_TIMEOUT
            if "mooncake" in torch_distributed_backend:
                from mooncake.pg import MooncakeBackendOptions

                pg_active_size = len(ranks)
                if not recovered_rank and max_world_size is not None:
                    assert max_world_size >= len(ranks), (
                        f"max_world_size ({max_world_size}) must be >= "
                        f"group size ({len(ranks)})"
                    )
                    pg_active_size = max_world_size

                pg_active_ranks = torch.zeros(
                    pg_active_size, dtype=torch.int32, device=self.device
                )
                pg_active_ranks[: len(ranks)] = 1
                pg_active_ranks_cpu = torch.zeros(pg_active_size, dtype=torch.int32)
                pg_active_ranks_cpu[: len(ranks)] = 1

                if not recovered_rank and max_world_size is not None:
                    dev_opts = MooncakeBackendOptions(
                        pg_active_ranks, recovered_rank, max_world_size
                    )
                    cpu_opts = MooncakeBackendOptions(
                        pg_active_ranks_cpu, recovered_rank, max_world_size
                    )
                else:
                    dev_opts = MooncakeBackendOptions(pg_active_ranks, recovered_rank)
                    cpu_opts = MooncakeBackendOptions(
                        pg_active_ranks_cpu, recovered_rank
                    )

                active_ranks = pg_active_ranks[: len(ranks)]
                active_ranks_cpu = pg_active_ranks_cpu[: len(ranks)]
                device_group = torch.distributed.new_group(
                    ranks,
                    backend="mooncake",
                    pg_options=dev_opts,
                    timeout=subgroup_timeout,
                    group_desc=f"{group_name}:device",
                )
                cpu_group = torch.distributed.new_group(
                    ranks,
                    backend="mooncake-cpu",
                    pg_options=cpu_opts,
                    timeout=subgroup_timeout,
                    group_desc=f"{group_name}:cpu",
                )
            else:
                active_ranks = torch.ones(
                    len(ranks), dtype=torch.int32, device=self.device
                )
                active_ranks_cpu = torch.ones(len(ranks), dtype=torch.int32)
                pg_options = get_torch_distributed_pg_options(group_name)
                device_group = torch.distributed.new_group(
                    ranks,
                    backend=torch_distributed_backend,
                    pg_options=pg_options,
                    timeout=subgroup_timeout,
                    group_desc=f"{group_name}:device",
                )
                # a group with `gloo` backend, to allow direct coordination
                # between processes through the CPU.
                cpu_group = torch.distributed.new_group(
                    ranks,
                    backend="gloo",
                    timeout=gloo_timeout,
                    group_desc=f"{group_name}:cpu",
                )
            if self.rank in ranks:
                self.ranks = ranks
                self.world_size = len(ranks)
                self.rank_in_group = ranks.index(self.rank)
                self.device_group = device_group
                self.cpu_group = cpu_group
                self.active_ranks = active_ranks
                self.active_ranks_cpu = active_ranks_cpu

        assert self.cpu_group is not None
        assert self.device_group is not None

        # Import communicators
        self.use_pynccl = use_pynccl
        self.use_pymscclpp = use_pymscclpp
        self.use_custom_allreduce = use_custom_allreduce
        self.use_torch_symm_mem_all_reduce = use_torch_symm_mem_all_reduce
        self.use_hpu_communicator = use_hpu_communicator
        self.use_xpu_communicator = use_xpu_communicator
        self.use_npu_communicator = use_npu_communicator
        self.use_message_queue_broadcaster = use_message_queue_broadcaster

        # Lazy import to avoid documentation build error
        from sglang.srt.distributed.device_communicators.custom_all_reduce import (
            dispatch_custom_allreduce,
        )
        from sglang.srt.distributed.device_communicators.pymscclpp import (
            PyMscclppCommunicator,
        )
        from sglang.srt.distributed.device_communicators.pynccl import (
            PyNcclCommunicator,
        )
        from sglang.srt.distributed.device_communicators.pynccl_allocator import (
            debug_check_symmetric_mempool,
            is_symmetric_memory_enabled,
            use_symmetric_memory,
        )
        from sglang.srt.distributed.device_communicators.torch_symm_mem import (
            TorchSymmMemCommunicator,
        )
        from sglang.srt.layers.dp_attention import is_allocation_symmetric

        self.is_symmetric_memory_enabled = is_symmetric_memory_enabled
        self.use_symmetric_memory = use_symmetric_memory
        self.is_allocation_symmetric = is_allocation_symmetric
        self.debug_check_symmetric_mempool = debug_check_symmetric_mempool
        if is_hip():
            from sglang.srt.distributed.device_communicators.quick_all_reduce import (
                QuickAllReduce,
                qr_rocm_arch_available,
            )

        self.pynccl_comm: Optional[PyNcclCommunicator] = None
        if use_pynccl and self.world_size > 1:
            self.pynccl_comm = PyNcclCommunicator(
                group=self.cpu_group,
                device=self.device,
                is_symmetric_memory_enabled=self.is_symmetric_memory_enabled(),
            )

        self.pymscclpp_comm: Optional[PyMscclppCommunicator] = None
        if use_pymscclpp and self.world_size > 1:
            self.pymscclpp_comm = PyMscclppCommunicator(
                group=self.cpu_group,
                device=self.device,
            )

        self.ca_comm: Optional[Any] = None
        self.qr_comm: Optional[QuickAllReduce] = None
        if use_custom_allreduce and self.world_size > 1:
            # Initialize a custom fast all-reduce implementation.
            try:
                CAClass = dispatch_custom_allreduce(
                    group=self.cpu_group,
                    device=self.device,
                )
                self.ca_comm = CAClass(
                    group=self.cpu_group,
                    device=self.device,
                )
            except Exception as e:
                logger.warning(
                    f"Setup Custom allreduce failed with {e}. To silence this "
                    "warning, specify --disable-custom-all-reduce explicitly."
                )

            if is_hip():
                try:
                    # Initialize a custom quick all-reduce implementation for AMD
                    # when rocm >= gfx942. Quick reduce is designed as a
                    # complement to custom allreduce.
                    # Based on quickreduce (https://github.com/mk1-project/quickreduce).
                    if qr_rocm_arch_available():
                        self.qr_comm = QuickAllReduce(
                            group=self.cpu_group, device=self.device
                        )
                except Exception as e:
                    logger.warning(f"Failed to initialize QuickAllReduce: {e}")
        elif self.world_size > 1 and is_hip():
            logger.info("[AR] All-reduce call path: NCCL (custom AR disabled)")

        self.torch_symm_mem_comm: Optional[TorchSymmMemCommunicator] = None
        if self.use_torch_symm_mem_all_reduce and self.world_size > 1:
            self.torch_symm_mem_comm = TorchSymmMemCommunicator(
                group=self.cpu_group,
                device=self.device,
            )

        # Create communicator for other hardware backends
        from sglang.srt.distributed.device_communicators.hpu_communicator import (
            HpuCommunicator,
        )
        from sglang.srt.distributed.device_communicators.npu_communicator import (
            NpuCommunicator,
        )
        from sglang.srt.distributed.device_communicators.xpu_communicator import (
            XpuCommunicator,
        )

        self.hpu_communicator: Optional[HpuCommunicator] = None
        if use_hpu_communicator and self.world_size > 1:
            self.hpu_communicator = HpuCommunicator(group=self.device_group)

        self.xpu_communicator: Optional[XpuCommunicator] = None
        if use_xpu_communicator and self.world_size > 1:
            self.xpu_communicator = XpuCommunicator(group=self.device_group)

        self.npu_communicator: Optional[NpuCommunicator] = None
        if use_npu_communicator and self.world_size > 1:
            self.npu_communicator = NpuCommunicator(group=self.device_group)

        # Create message queue
        from sglang.srt.distributed.device_communicators.shm_broadcast import (
            MessageQueue,
        )

        self.mq_broadcaster: Optional[MessageQueue] = None
        if use_message_queue_broadcaster and self.world_size > 1 and not recovered_rank:
            # Recovered ranks create their mq_broadcaster in elastic_ep.py
            self.mq_broadcaster = MessageQueue.create_from_process_group(
                self.cpu_group, 1 << 22, 6
            )

    def __repr__(self):
        return (
            f"ranks={self.ranks} rank={self.rank} local_rank={self.local_rank} use_pynccl={self.use_pynccl} "
            f"device_group={self.device_group} cpu_group={self.cpu_group} unique_name={self.unique_name} "
            f"world_size={self.world_size} rank_in_group={self.rank_in_group}"
        )

    @property
    def first_rank(self):
        """Return the global rank of the first process in the group"""
        return self.ranks[0]

    @property
    def last_rank(self):
        """Return the global rank of the last process in the group"""
        return self.ranks[-1]

    @property
    def is_first_rank(self):
        """Return whether the caller is the first process in the group"""
        return self.rank == self.first_rank

    @property
    def is_last_rank(self):
        """Return whether the caller is the last process in the group"""
        return self.rank == self.last_rank

    @property
    def next_rank(self):
        """Return the global rank of the process that follows the caller"""
        rank_in_group = self.rank_in_group
        world_size = self.world_size
        return self.ranks[(rank_in_group + 1) % world_size]

    @property
    def prev_rank(self):
        """Return the global rank of the process that precedes the caller"""
        rank_in_group = self.rank_in_group
        world_size = self.world_size
        return self.ranks[(rank_in_group - 1) % world_size]

    @contextmanager
    def graph_capture(
        self,
        graph_capture_context: Optional[GraphCaptureContext] = None,
        stream=None,
    ):
        if graph_capture_context is None:
            if stream is None:
                stream = self.device_module.Stream()
            graph_capture_context = GraphCaptureContext(stream)
        else:
            stream = graph_capture_context.stream
        # We don't need the context of custom quick allreduce because the ipc access
        # is already collected in init() and we can capture the quick allreduce directly.
        ca_comm = self.ca_comm
        maybe_ca_context = nullcontext() if ca_comm is None else ca_comm.capture()

        # ensure all initialization operations complete before attempting to
        # capture the graph on another stream
        curr_stream = get_current_device_stream_fast()
        if curr_stream != stream:
            stream.wait_stream(curr_stream)

        with self.device_module.stream(stream), maybe_ca_context:
            # In graph mode, we have to be very careful about the collective
            # operations. The current status is:
            #     allreduce \ Mode   |  Eager  |  Graph  |
            # --------------------------------------------
            # quick allreduce        | enabled | enabled |
            # custom allreduce       | enabled | enabled |
            # PyNccl                 | disabled| enabled |
            # PyMscclpp              | disabled| enabled |
            # TorchSymmMem           | disabled| enabled |
            # torch.distributed      | enabled | disabled|
            #
            # Note: When custom quick allreduce is enabled, a runtime check
            #  will be performed. If the tensor size is too small, it will
            #  automatically fall back to the next available option.
            # Note that custom allreduce will have a runtime check, if the
            #  tensor size is too large, it will fallback to the next
            #  available option.
            # Note that the PyMsccl needs to register the tensor in ahead,
            #  which will introduce large overhead in the eager case,
            #  therefore it is only supported in the graph case.
            # In summary: We select the appropriate allreduce method for
            #  each mode based on the algorithm order in the table and
            #  their usage conditions.
            pynccl_comm = self.pynccl_comm
            maybe_pynccl_context: Any
            if not pynccl_comm:
                maybe_pynccl_context = nullcontext()
            else:
                maybe_pynccl_context = pynccl_comm.change_state(enable=True)

            pymscclpp_comm = self.pymscclpp_comm
            maybe_pymscclpp_context: Any
            if not pymscclpp_comm:
                maybe_pymscclpp_context = nullcontext()
            else:
                maybe_pymscclpp_context = pymscclpp_comm.change_state(enable=True)
            with maybe_pynccl_context, maybe_pymscclpp_context:
                yield graph_capture_context

    def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
        """
        User-facing all-reduce function before we actually call the
        all-reduce operation.

        We need this because Dynamo does not support passing an arbitrary
        object (`self` in this case) to a custom op. We need to pass the
         group name as a string, and then look up the group coordinator from
         the group name, dispatch the all-reduce operation to the group
         coordinator.

        In addition, PyTorch custom ops do not support mutation or returning
        a new tensor in the same op. So we need to figure out if the op is
        in-place or out-of-place ahead of time — except under Dynamo tracing,
        where the method selection would guard on the symbolic shape; there we
        always emit the out-of-place op with method "auto" and resolve the
        method at runtime inside the op.
        """
        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return input_

        if input_.is_cpu:
            if is_shm_available(input_.dtype, self.world_size, self.local_size):
                torch.ops.sgl_kernel.shm_allreduce(input_, REDUCE_OP_SUM)
            else:
                torch.distributed.all_reduce(input_, group=self.device_group)
            return input_

        if self.hpu_communicator is not None and not self.hpu_communicator.disabled:
            return self.hpu_communicator.all_reduce(input_)

        if self.xpu_communicator is not None and not self.xpu_communicator.disabled:
            # Route through inplace_all_reduce custom op so Dynamo treats this as
            # an opaque call and does not decompose it into _c10d_functional primitives
            # (which invoke sycl_event.wait() and break XPU graph capture).
            # Keeps the operation in-place; the all-reduce is performed by
            # _all_reduce_in_place, which for XPU falls through to
            # torch.distributed.all_reduce on self.device_group (the same group
            # used by xpu_communicator).
            inplace_all_reduce(input_, group_name=self.unique_name)
            return input_

        if self.npu_communicator is not None and not self.npu_communicator.disabled:
            return self.npu_communicator.all_reduce(input_)

        if torch.compiler.is_compiling():
            # Byte-size thresholds in method selection (e.g. `_pick_algo` or
            # `should_mscclpp_allreduce`) would guard on the symbolic token dim
            # and recompile per shape; defer the selection to runtime inside
            # the opaque custom op. Groups without any accelerated
            # communicator keep the inplace split op so their collective
            # stays outside captured graphs. The symmetric-memory in-place
            # path below is deliberately bypassed under compile: its raw
            # pynccl call is untraceable (hard error with fullgraph, graph
            # break otherwise) and its in-place contract does not fit the
            # outplace custom op.
            if (
                self.ca_comm is None
                and self.qr_comm is None
                and self.pymscclpp_comm is None
                and self.torch_symm_mem_comm is None
                and self.pynccl_comm is None
            ):
                inplace_all_reduce(input_, group_name=self.unique_name)
                return input_
            return outplace_all_reduce(
                input_,
                group_name=self.unique_name,
                outplace_all_reduce_method="auto",
            )

        should_use_pymscclpp_allreduce = (
            self.pymscclpp_comm is not None
            and self.pymscclpp_comm.should_mscclpp_allreduce(input_)
        )
        # With the MNNVL opt-in, let CustomAllReduceV2 take eligible (small)
        # inputs ahead of the symm-mem pynccl fast path; otherwise pynccl
        # would absorb every all-reduce whenever --enable-symm-mem is on and
        # v2 never runs. Large inputs fail should_custom_ar and still go to
        # the symm-mem path below.
        _ca_takes_input = (
            _CA_V2_MULTINODE
            and self.ca_comm is not None
            and not self.ca_comm.disabled
            and self.ca_comm.should_custom_ar(input_)
        )
        if (
            self.pynccl_comm is not None
            and self.is_symmetric_memory_enabled()
            and not should_use_pymscclpp_allreduce
            and not _ca_takes_input
        ):
            self.debug_check_symmetric_mempool(self, {"input": input_}, "all_reduce")
            with self.pynccl_comm.change_state(enable=True):
                self.pynccl_comm.all_reduce(input_)
                return input_

        outplace_all_reduce_method = self._resolve_outplace_all_reduce_method(
            input_=input_,
            should_use_pymscclpp_allreduce=should_use_pymscclpp_allreduce,
        )
        if outplace_all_reduce_method is not None:
            return outplace_all_reduce(
                input_,
                group_name=self.unique_name,
                outplace_all_reduce_method=outplace_all_reduce_method,
            )
        else:
            inplace_all_reduce(input_, group_name=self.unique_name)
            return input_

    def quant_all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
        """
        User-facing quant-all-reduce function similar to all-reduce. (NPU support only)
        """
        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return input_

        if self.npu_communicator is not None and not self.npu_communicator.disabled:
            return self.npu_communicator.quant_all_reduce(input_)
        else:
            inplace_all_reduce(input_, group_name=self.unique_name)
            return input_

    def fused_allreduce_rmsnorm(
        self,
        input_: torch.Tensor,
        residual_inp_: torch.Tensor,
        weight_: torch.Tensor,
        eps: float,
    ) -> Optional[Tuple[torch.Tensor, torch.Tensor]]:
        """Attempt fused all-reduce + RMSNorm via custom all-reduce communicator. ROCm/HIP Only"""
        ca_comm = self.ca_comm
        if ca_comm is None or getattr(ca_comm, "disabled", True):
            return None

        # Prefer communicator-native fused API when provided.
        if hasattr(ca_comm, "fused_allreduce_rmsnorm"):
            try:
                return ca_comm.fused_allreduce_rmsnorm(
                    input_, residual_inp_, weight_, eps
                )
            except Exception:
                # Fall back to custom_fused_ar_rms path below.
                pass

        if not hasattr(ca_comm, "custom_fused_ar_rms"):
            return None

        # 1-stage vs 2-stage selection for fused AR+RMSNorm:
        # The 1-stage kernel launches one block per token and is capped at
        # 80 tokens (kMaxBlocks).  Guard with a byte threshold so large
        # prefill batches fall through to the 2-stage kernel instead of
        # hitting a runtime error.  AITER's C++ dispatch already gates
        # which hidden_dims have valid 1-stage support.
        if envs.SGLANG_USE_1STAGE_ALLREDUCE.is_set():
            use_1stage_ar = envs.SGLANG_USE_1STAGE_ALLREDUCE.get()
        else:
            total_bytes = input_.numel() * input_.element_size()
            use_1stage_ar = total_bytes <= 128 * 1024

        if (
            getattr(ca_comm, "_IS_CAPTURING", False)
            and not torch.cuda.is_current_stream_capturing()
            and is_in_tc_piecewise_cuda_graph()
        ):
            if not hasattr(ca_comm, "fused_ar_rms"):
                return None
            return ca_comm.fused_ar_rms(
                input_,
                residual_inp_,
                w=weight_,
                eps=eps,
                registered=False,
                use_1stage=use_1stage_ar,
            )
        fused_outputs = ca_comm.custom_fused_ar_rms(
            input_,
            residual_inp_,
            weight_,
            eps,
            use_1stage_ar,
        )
        return fused_outputs

    def fused_allreduce_rmsnorm_quant_per_group(
        self,
        input_: torch.Tensor,
        residual_inp_: torch.Tensor,
        weight_: torch.Tensor,
        eps: float,
        group_size: int = 128,
        emit_bf16: bool = False,
    ) -> Optional[Tuple[torch.Tensor, ...]]:
        """Attempt fused all-reduce + RMSNorm + per-group FP8 quant.

        ROCm/aiter/gfx95-only entry point. Returns ``None`` on any other
        platform or when the aiter custom-all-reduce communicator cannot
        service the request, letting the caller fall back to the existing
        ``fused_allreduce_rmsnorm`` + separate per-group quant path.

        When ``emit_bf16=True`` the fused kernel also writes the
        pre-quantization bf16/fp16 normed output and returns
        ``(fp8, residual_out, scale, bf16)`` — used by GDN-style layers that
        need both an FP8 projection and a bf16 gating projection without
        launching a separate per-group quant kernel.
        """
        if not (is_hip() and is_gfx95_supported()):
            return None

        ca_comm = self.ca_comm
        if ca_comm is None or getattr(ca_comm, "disabled", True):
            return None
        if not hasattr(ca_comm, "custom_fused_ar_rms_per_group_quant"):
            return None

        # Shape / size eligibility mirrors aiter's internal gate so we fail
        # fast without entering the HIP kernel dispatch.
        K = input_.shape[-1]
        if K % group_size != 0 or K > 16384:
            return None
        total_bytes = input_.numel() * input_.element_size()
        if total_bytes == 0 or total_bytes > 8 * 1024 * 8192:
            return None
        if self.world_size == 6:
            return None

        if envs.SGLANG_USE_1STAGE_ALLREDUCE.is_set():
            use_1stage_ar = envs.SGLANG_USE_1STAGE_ALLREDUCE.get()
        else:
            token_num = input_.numel() // K
            use_1stage_ar = total_bytes <= 128 * 1024
            if (
                # Keep the default 128 KiB cutoff except for the measured TP=8
                # K=7168 graph-replay crossover. K=4096 remains on the default
                # rule because token_num=8/16 still favored 1-stage there.
                self.world_size == 8
                and 4096 < K <= 7168
                and token_num >= 8
                and use_1stage_ar
            ):
                use_1stage_ar = False

        try:
            return ca_comm.custom_fused_ar_rms_per_group_quant(
                input_,
                residual_inp_,
                weight_,
                eps,
                group_size,
                use_1stage_ar,
                emit_bf16=emit_bf16,
            )
        except Exception:
            return None

    def _resolve_outplace_all_reduce_method(
        self,
        input_: torch.Tensor,
        should_use_pymscclpp_allreduce: Optional[bool] = None,
    ) -> Optional[str]:
        if should_use_pymscclpp_allreduce is None:
            should_use_pymscclpp_allreduce = (
                self.pymscclpp_comm is not None
                and self.pymscclpp_comm.should_mscclpp_allreduce(input_)
            )
        if (
            self.ca_comm is not None
            and not self.ca_comm.disabled
            and not should_use_pymscclpp_allreduce
            and self.ca_comm.should_custom_ar(input_)
        ):
            return "ca"
        if (
            self.qr_comm is not None
            and not self.qr_comm.disabled
            and self.qr_comm.should_quick_allreduce(input_)
        ):
            return "qr"
        if self.pymscclpp_comm is not None and should_use_pymscclpp_allreduce:
            return "pymscclpp"
        if (
            self.torch_symm_mem_comm is not None
            and not self.torch_symm_mem_comm.disabled
            and self.torch_symm_mem_comm.should_torch_symm_mem_allreduce(input_)
        ):
            return "torch_symm_mem"
        if is_in_tc_piecewise_cuda_graph() and self.pynccl_comm is not None:
            # For piecewise cuda graph, we use pynccl outplace allreduce
            return "pynccl"
        return None

    def _all_reduce_out_place(
        self, input_: torch.Tensor, outplace_all_reduce_method: str
    ) -> torch.Tensor:
        if outplace_all_reduce_method == "auto":
            outplace_all_reduce_method = self._resolve_outplace_all_reduce_method(
                input_
            )
            if outplace_all_reduce_method == "pymscclpp":
                # pymscclpp reduces in place and returns its input; feed it a
                # clone to honor the op's no-mutation contract.
                input_ = input_.clone()
            elif outplace_all_reduce_method is None:
                # Force pynccl over the in-place fallback: it is graph-capture
                # safe and NCCL is natively out-of-place, avoiding the clone
                # the in-place fallback needs.
                if self.pynccl_comm is not None:
                    outplace_all_reduce_method = "pynccl"
                else:
                    out = input_.clone()
                    self._all_reduce_in_place(out)
                    return out
        ca_comm = self.ca_comm
        qr_comm = self.qr_comm
        pymscclpp_comm = self.pymscclpp_comm
        torch_symm_mem_comm = self.torch_symm_mem_comm
        pynccl_comm = self.pynccl_comm
        assert any([qr_comm, ca_comm, pymscclpp_comm, torch_symm_mem_comm, pynccl_comm])
        if outplace_all_reduce_method == "ca":
            assert not ca_comm.disabled
            out = ca_comm.custom_all_reduce(input_)
        elif outplace_all_reduce_method == "qr":
            assert not qr_comm.disabled
            out = qr_comm.quick_all_reduce(input_)
        elif outplace_all_reduce_method == "torch_symm_mem":
            assert not torch_symm_mem_comm.disabled
            out = torch_symm_mem_comm.all_reduce(input_)
        elif outplace_all_reduce_method == "pymscclpp":
            assert not pymscclpp_comm.disabled
            out = pymscclpp_comm.all_reduce(input_)
        elif outplace_all_reduce_method == "pynccl":
            with pynccl_comm.change_state(enable=True):
                out = pynccl_comm.outplace_all_reduce(input_)
        assert out is not None
        return out

    def _all_reduce_in_place(self, input_: torch.Tensor) -> None:
        pynccl_comm = self.pynccl_comm
        torch_symm_mem_comm = self.torch_symm_mem_comm
        if pynccl_comm is not None and not pynccl_comm.disabled:
            pynccl_comm.all_reduce(input_)
        elif (
            torch_symm_mem_comm is not None
            and not torch_symm_mem_comm.disabled
            and torch_symm_mem_comm.should_torch_symm_mem_allreduce(input_)
        ):
            torch_symm_mem_comm.all_reduce(input_, out=input_)
        else:
            torch.distributed.all_reduce(input_, group=self.device_group)

    def reduce_scatter_along_dim(
        self, input_: torch.Tensor, dim: int = -1
    ) -> torch.Tensor:
        world_size = self.world_size
        # Bypass the function if we are using only 1 GPU.
        if world_size == 1:
            return input_
        assert (
            -input_.dim() <= dim < input_.dim()
        ), f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"

        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()

        with self.use_symmetric_memory(self):
            # TODO: make sure whether tensor layout affects nccl reduce_scatter
            # Note: This will produce an incorrect answer if we don't make
            # the input_tensor contiguous. Possible bug in reduce_scatter_tensor?
            input_tensor = input_.movedim(dim, 0).contiguous()

        assert input_tensor.shape[0] % world_size == 0
        chunk_size = input_tensor.shape[0] // world_size
        output_shape = (chunk_size,) + input_tensor.shape[1:]

        with self.use_symmetric_memory(self):
            output_tensor = torch.empty(
                output_shape,
                dtype=input_tensor.dtype,
                device=input_tensor.device,
            )

        self.reduce_scatter_tensor(output_tensor, input_tensor)

        # Reshape before returning
        return output_tensor.movedim(0, dim)

    def _reduce_scatter_tensor(
        self,
        output: torch.Tensor,
        input: torch.Tensor,
    ) -> torch.Tensor:
        pynccl_comm = self.pynccl_comm
        if pynccl_comm is not None and (
            not pynccl_comm.disabled or self.is_symmetric_memory_enabled()
        ):
            self.debug_check_symmetric_mempool(
                self, {"output": output, "input": input}, "reduce_scatter_tensor"
            )
            with pynccl_comm.change_state(enable=True):
                pynccl_comm.reduce_scatter(output, input)
        else:
            torch.distributed.reduce_scatter_tensor(
                output, input, group=self.device_group
            )
        return output

    def reduce_scatter_tensor(self, output: torch.Tensor, input: torch.Tensor):
        if _is_npu:
            self._reduce_scatter_tensor(output, input)
        elif self._maybe_aiter_reduce_scatter(output, input):
            return
        else:
            reg_reduce_scatter_tensor(output, input, group_name=self.unique_name)

    def _has_aiter_custom_reduce_scatter(self) -> bool:
        ca_comm = self.ca_comm
        return (
            ca_comm is not None
            and not getattr(ca_comm, "disabled", True)
            and hasattr(ca_comm, "should_custom_ar")
            and hasattr(ca_comm, "reduce_scatter")
        )

    def _maybe_aiter_reduce_scatter(
        self, output: torch.Tensor, input: torch.Tensor
    ) -> bool:
        # Aiter custom reduce-scatter (ROCm). Mirrors `_all_gather_into_tensor`'s
        # custom all-gather path: an equal-chunk (no variable sizes) reduce-scatter
        # using the registered symmetric-memory buffers, which is faster than the
        # generic RCCL kernel for the small, latency-bound decode collective.
        # Gated by SGLANG_DP_USE_REDUCE_SCATTER. Falls back (returns False)
        # for non-ROCm / unsupported shape/size/topology so the caller uses RCCL.
        if not (
            is_hip()
            and envs.SGLANG_DP_USE_REDUCE_SCATTER.get()
            and self._has_aiter_custom_reduce_scatter()
            and input.is_contiguous()
            and output.is_contiguous()
            and input.dtype in (torch.float32, torch.float16, torch.bfloat16)
        ):
            return False
        ca_comm = self.ca_comm
        # input is the full (pre-reduce) buffer; should_custom_ar bounds its size.
        if not ca_comm.should_custom_ar(input):
            return False
        # Equal-chunk only: input rows must split evenly into world_size chunks
        # matching the per-rank output rows.
        if input.shape[0] != output.shape[0] * self.world_size:
            return False
        if getattr(ca_comm, "_IS_CAPTURING", False):
            if torch.cuda.is_current_stream_capturing():
                ca_comm.reduce_scatter(input, output, registered=True)
            elif is_in_tc_piecewise_cuda_graph():
                ca_comm.reduce_scatter(input, output, registered=False)
            else:
                # True CUDA graph warmup: avoid a different host collective.
                output.zero_()
            return True
        ca_comm.reduce_scatter(input, output, registered=False)
        return True

    def _all_to_all_single(self, output: torch.Tensor, input: torch.Tensor) -> None:
        # pynccl path keeps the a2a exchange CUDA-graph-capturable (DCP a2a backend).
        pynccl_comm = self.pynccl_comm
        if pynccl_comm is not None and not pynccl_comm.disabled:
            pynccl_comm.all_to_all_single(output, input)
        else:
            torch.distributed.all_to_all_single(output, input, group=self.device_group)

    def all_to_all_single(self, output: torch.Tensor, input: torch.Tensor):
        if self.world_size == 1:
            output.copy_(input)
            return
        reg_all_to_all_single(output, input, group_name=self.unique_name)

    def reduce_scatter(
        self,
        output: torch.Tensor,
        input_list: List[torch.Tensor],
    ) -> None:
        # TODO(ch-wan): support other backends
        torch.distributed.reduce_scatter(output, input_list, group=self.device_group)
        return output

    def reduce_scatterv(
        self,
        input_: torch.Tensor,
        output: Optional[torch.Tensor] = None,
        sizes: Optional[List[int]] = None,
    ) -> torch.Tensor:
        world_size = self.world_size
        pynccl_comm = self.pynccl_comm

        with pynccl_comm.change_state(enable=True):
            assert (
                pynccl_comm is not None and not pynccl_comm.disabled
            ), "pynccl is required for reduce_scatterv"

            if sizes is not None:
                assert len(sizes) == world_size
                assert input_.shape[0] == sum(sizes)
                chunk_size = sizes[self.rank_in_group]
            else:
                assert input_.shape[0] % world_size == 0
                chunk_size = input_.shape[0] // world_size
            output_shape = (chunk_size,) + input_.shape[1:]

            if output is None:
                output = torch.empty(
                    output_shape, dtype=input_.dtype, device=input_.device
                )
            else:
                assert output.shape == output_shape

            pynccl_comm.reduce_scatter(output, input_, sizes=sizes)
            return output

    def _all_gather_into_tensor(self, output: torch.Tensor, input: torch.Tensor):
        # Aiter custom all-gather (ROCm). Set SGLANG_USE_AITER_AG=0 to disable.
        # Aiter's should_custom_ag still owns shape/layout validation:
        # 16B alignment, weak-contiguous, supported topology, and per-rank
        # size <= max_size/(world*2).
        # On a hit, writes directly into the caller's pre-allocated `output` via
        # all_gather_reg during CUDA-graph capture, and all_gather_unreg
        # under torch_memory_saver and other paths.
        ca_comm = self.ca_comm
        if (
            is_hip()
            and envs.SGLANG_USE_AITER_AG.get()
            and self._has_aiter_custom_all_gather()
            and input.is_contiguous()
            and output.is_contiguous()
            and input.dtype in (torch.float32, torch.float16, torch.bfloat16)
            and ca_comm.should_custom_ag(input)
        ):
            if getattr(ca_comm, "_IS_CAPTURING", False):
                if torch.cuda.is_current_stream_capturing():
                    if envs.SGLANG_MEMORY_SAVER_CUDA_GRAPH.get():
                        ca_comm.all_gather_unreg(input, out=output, dim=0)
                    else:
                        ca_comm.all_gather_reg(input, out=output, dim=0)
                elif is_in_tc_piecewise_cuda_graph():
                    ca_comm.all_gather_unreg(input, out=output, dim=0)
                else:
                    # True CUDA graph warmup: avoid a different host collective.
                    output.zero_()
                return
            else:
                ca_comm.all_gather_unreg(input, out=output, dim=0)
                return

        pynccl_comm = self.pynccl_comm
        if pynccl_comm is not None and (
            not pynccl_comm.disabled or self.is_symmetric_memory_enabled()
        ):
            self.debug_check_symmetric_mempool(
                self, {"output": output}, "all_gather_into_tensor"
            )
            with pynccl_comm.change_state(enable=True):
                pynccl_comm.all_gather(output, input)
        else:
            torch.distributed.all_gather_into_tensor(
                output, input, group=self.device_group
            )

    def _has_aiter_custom_all_gather(self) -> bool:
        if self._deterministic_collectives_enabled():
            return False
        ca_comm = self.ca_comm
        return (
            ca_comm is not None
            and not getattr(ca_comm, "disabled", True)
            and hasattr(ca_comm, "should_custom_ag")
            and hasattr(ca_comm, "all_gather_reg")
            and hasattr(ca_comm, "all_gather_unreg")
        )

    @staticmethod
    def _deterministic_collectives_enabled() -> bool:
        if envs.SGLANG_USE_1STAGE_ALLREDUCE.is_set():
            return envs.SGLANG_USE_1STAGE_ALLREDUCE.get()
        return envs.SGLANG_ENABLE_DETERMINISTIC_INFERENCE.get()

    def all_gather_into_tensor(self, output: torch.Tensor, input: torch.Tensor):
        if _is_npu:
            self._all_gather_into_tensor(output, input)
        else:
            # XPU and CUDA both go through reg_all_gather_into_tensor (custom_op) to
            # stay opaque to Dynamo. Calling torch.distributed.all_gather_into_tensor
            # directly causes Dynamo to rewrite it as _c10d_functional.all_gather_into_tensor
            # + wait_tensor, which invokes sycl_event.wait() and breaks XPU graph capture.
            reg_all_gather_into_tensor(output, input, group_name=self.unique_name)

    def all_gather(
        self,
        input_: torch.Tensor,
        dim: int = -1,
        output_tensor_list: Optional[List[torch.Tensor]] = None,
    ) -> torch.Tensor:
        world_size = self.world_size
        # Bypass the function if we are using only 1 GPU.
        if world_size == 1:
            if output_tensor_list is not None:
                logger.warning(
                    "Performing in-place all-gather with a group size of 1. "
                    "This may be unnecessary; consider bypassing it for better efficiency."
                )
                output_tensor_list[0].copy_(input_)
                return None
            else:
                return input_

        if output_tensor_list is not None:
            # TODO(ch-wan): support other backends
            return torch.distributed.all_gather(
                output_tensor_list, input_, group=self.device_group
            )

        assert (
            -input_.dim() <= dim < input_.dim()
        ), f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"

        # For HPUs, use HPU communicator.
        hpu_comm = self.hpu_communicator
        if hpu_comm is not None and not hpu_comm.disabled:
            return hpu_comm.all_gather(input_, dim)

        # For NPUs, use NPU communicator.
        npu_comm = self.npu_communicator
        if npu_comm is not None and not npu_comm.disabled:
            return npu_comm.all_gather(input_, dim)

        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()
        input_size = input_.size()
        # NOTE: we have to use concat-style all-gather here,
        # stack-style all-gather has compatibility issues with
        # torch.compile . see https://github.com/pytorch/pytorch/issues/138795
        output_size = (input_size[0] * world_size,) + input_size[1:]
        # Allocate output tensor.
        with self.use_symmetric_memory(
            self, disabled=not self.is_allocation_symmetric()
        ):
            output_tensor = torch.empty(
                output_size, dtype=input_.dtype, device=input_.device
            )

        # All-gather.
        if input_.is_cpu:
            if is_shm_available(input_.dtype, self.world_size, self.local_size):
                return torch.ops.sgl_kernel.shm_allgather(input_, dim)
            else:
                torch.distributed.all_gather_into_tensor(
                    output_tensor, input_, group=self.device_group
                )
        else:
            self.all_gather_into_tensor(output_tensor, input_)

        # Reshape
        output_tensor = output_tensor.reshape((world_size,) + input_size)
        output_tensor = output_tensor.movedim(0, dim)
        output_tensor = output_tensor.reshape(
            input_size[:dim] + (world_size * input_size[dim],) + input_size[dim + 1 :]
        )
        return output_tensor

    def all_gatherv(
        self,
        input_: Union[torch.Tensor, List[torch.Tensor]],
        sizes: Optional[List[int]] = None,
        output: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, List[torch.Tensor]]:
        """
        Supports varying sizes per rank and input tensor list.
        `sizes`: a list of len(world_size) with the number of items per rank to gather.
        `output`: optional pre-allocated destination buffer (single-tensor input only).
            When given, NCCL writes the gathered result directly into it, avoiding an
            extra output allocation + caller-side copy.
        """
        world_size = self.world_size
        pynccl_comm = self.pynccl_comm

        with pynccl_comm.change_state(enable=True):
            assert (
                pynccl_comm is not None and not pynccl_comm.disabled
            ), "pynccl is required for all_gatherv"

            def _all_gather_allocate_output(
                input_: torch.Tensor,
                sizes: Optional[List[int]] = None,
                output: Optional[torch.Tensor] = None,
            ):
                input_size = input_.size()
                if sizes is not None:
                    assert len(sizes) == world_size
                    assert input_.shape[0] == sizes[self.rank_in_group]
                    output_size = (sum(sizes),) + input_size[1:]
                    # 'sizes' is not needed if all inputs in the same group have the same shape
                    if all(s == sizes[0] for s in sizes):
                        sizes = None
                else:
                    output_size = (input_size[0] * world_size,) + input_size[1:]
                if output is not None:
                    assert tuple(output.shape) == tuple(output_size), (
                        f"all_gatherv output buffer shape {tuple(output.shape)} "
                        f"!= expected {tuple(output_size)}"
                    )
                    return output, sizes
                # Allocate output tensor.
                with self.use_symmetric_memory(self, disabled=sizes is not None):
                    output_tensor = torch.empty(
                        output_size, dtype=input_.dtype, device=input_.device
                    )
                return output_tensor, sizes

            single_input = isinstance(input_, torch.Tensor)
            if single_input:
                input_ = [input_]
            elif output is not None:
                raise ValueError("all_gatherv `output` requires a single-tensor input")

            output_list = []
            size_list = []
            for inp in input_:
                output_tensor, s = _all_gather_allocate_output(
                    inp, sizes=sizes, output=output
                )
                output_list.append(output_tensor)
                size_list.append(s)

            pynccl_comm.group_start()
            for i, inp in enumerate(input_):
                pynccl_comm.all_gather(output_list[i], inp, sizes=size_list[i])
            pynccl_comm.group_end()

            return output_list

    def gather(
        self, input_: torch.Tensor, dst: int = 0, dim: int = -1
    ) -> Optional[torch.Tensor]:
        """
        NOTE: We assume that the input tensor is on the same device across
        all the ranks.
        NOTE: `dst` is the local rank of the destination rank.
        """
        world_size = self.world_size
        # Bypass the function if we are using only 1 GPU.
        if world_size == 1:
            return input_
        assert (
            -input_.dim() <= dim < input_.dim()
        ), f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()
        if self.xpu_communicator is not None and not self.xpu_communicator.disabled:
            return self.xpu_communicator.gather(input_, self.rank_in_group, dst, dim)
        # Allocate output tensor.
        if self.rank_in_group == dst:
            gather_list = [torch.empty_like(input_) for _ in range(world_size)]
        else:
            gather_list = None
        # Gather.
        torch.distributed.gather(
            input_, gather_list, dst=self.ranks[dst], group=self.device_group
        )
        if self.rank_in_group == dst:
            output_tensor = torch.cat(gather_list, dim=dim)
        else:
            output_tensor = None
        return output_tensor

    def broadcast(self, input_: torch.Tensor, src: int = 0):
        """Broadcast the input tensor.
        NOTE: `src` is the local rank of the source rank.
        """
        assert src < self.world_size, f"Invalid src rank ({src})"

        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return input_
        # Broadcast.
        torch.distributed.broadcast(
            input_, src=self.ranks[src], group=self.device_group
        )
        return input_

    def broadcast_object(self, obj: Optional[Any] = None, src: int = 0):
        """Broadcast the input object.
        NOTE: `src` is the local rank of the source rank.
        """
        assert src < self.world_size, f"Invalid src rank ({src})"

        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return obj
        if self.mq_broadcaster is not None:
            assert src == 0, "Message queue broadcaster only supports src=0"
            return self.mq_broadcaster.broadcast_object(obj)
        if self.rank_in_group == src:
            torch.distributed.broadcast_object_list(
                [obj], src=self.ranks[src], group=self.cpu_group
            )
            return obj
        else:
            recv = [None]
            torch.distributed.broadcast_object_list(
                recv, src=self.ranks[src], group=self.cpu_group
            )
            return recv[0]

    def broadcast_object_list(
        self, obj_list: List[Any], src: int = 0, group: Optional[ProcessGroup] = None
    ):
        """Broadcast the input object list.
        NOTE: `src` is the local rank of the source rank.
        """
        assert src < self.world_size, f"Invalid src rank ({src})"

        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return obj_list
        # Broadcast.
        torch.distributed.broadcast_object_list(
            obj_list, src=self.ranks[src], group=self.device_group
        )
        return obj_list

    def all_gather_object(self, obj: Any) -> List[Any]:
        objs = [None] * self.world_size
        torch.distributed.all_gather_object(objs, obj, group=self.cpu_group)
        return objs

    def send_object(
        self,
        obj: Any,
        dst: int,
        async_send: bool = False,
        tag: int = 0,
    ) -> List[P2PWork]:
        """
        Send the input object list to the destination rank.
        This function uses the CPU group for all communications.

        TODO: If you want to use GPU communication, please add a new argument (e.g., data_group, group),
        use other functions (e.g., send), or implement a new function (e.g., send_object_device).

        NOTE: `dst` is the local rank of the destination rank.
        """

        assert dst < self.world_size, f"Invalid dst rank ({dst})"
        assert dst != self.rank_in_group, (
            "Invalid destination rank. Destination rank is the same "
            "as the current rank."
        )
        send_func = torch.distributed.isend if async_send else torch.distributed.send

        # Serialize object to tensor and get the size as well
        object_tensor = torch.frombuffer(pickle.dumps(obj), dtype=torch.uint8)
        size_tensor = torch.tensor(
            [object_tensor.numel()], dtype=torch.long, device="cpu"
        )

        # Send object size
        p2p_work = []
        size_work = send_func(
            size_tensor,
            self.ranks[dst],
            group=self.cpu_group,
            tag=tag,
        )
        if async_send:
            p2p_work.append(P2PWork(size_work, size_tensor))

        object_work = send_func(
            object_tensor,
            self.ranks[dst],
            group=self.cpu_group,
            tag=tag,
        )
        if async_send:
            p2p_work.append(P2PWork(object_work, object_tensor))

        return p2p_work

    def recv_object(
        self,
        src: int,
        tag: int = 0,
    ) -> Any:
        """Receive the input object list from the source rank."""
        """NOTE: `src` is the local rank of the source rank."""

        assert src < self.world_size, f"Invalid src rank ({src})"
        assert (
            src != self.rank_in_group
        ), "Invalid source rank. Source rank is the same as the current rank."

        size_tensor = torch.empty(1, dtype=torch.long, device="cpu")

        # Receive object size
        # We have to use irecv here to make it work for both isend and send.
        work = torch.distributed.irecv(
            size_tensor, src=self.ranks[src], group=self.cpu_group, tag=tag
        )
        work.wait()

        # Tensor to receive serialized objects into.
        object_tensor: Any = torch.empty(  # type: ignore[call-overload]
            size_tensor.item(),  # type: ignore[arg-type]
            dtype=torch.uint8,
            device="cpu",
        )

        work = torch.distributed.irecv(
            object_tensor, src=self.ranks[src], group=self.cpu_group, tag=tag
        )
        work.wait()

        obj = pickle.loads(object_tensor.numpy())
        return obj

    def broadcast_tensor_dict(
        self,
        tensor_dict: Optional[Dict[str, Union[torch.Tensor, Any]]] = None,
        src: int = 0,
        group: Optional[ProcessGroup] = None,
        metadata_group: Optional[ProcessGroup] = None,
    ) -> Optional[Dict[str, Union[torch.Tensor, Any]]]:
        """Broadcast the input tensor dictionary.
        NOTE: `src` is the local rank of the source rank.
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return tensor_dict

        group = self.device_group
        metadata_group = self.cpu_group
        assert src < self.world_size, f"Invalid src rank ({src})"

        rank_in_group = self.rank_in_group
        if rank_in_group == src:
            metadata_list: List[Tuple[Any, Any]] = []
            assert isinstance(
                tensor_dict, dict
            ), f"Expecting a dictionary, got {type(tensor_dict)}"
            metadata_list, tensor_list = _split_tensor_dict(tensor_dict)
            # `metadata_list` lives in CPU memory.
            # `broadcast_object_list` has serialization & deserialization,
            # all happening on CPU. Therefore, we can use the CPU group.
            self.broadcast_object(metadata_list, src=src)
            async_handles = []
            for tensor in tensor_list:
                if tensor.numel() == 0:
                    # Skip broadcasting empty tensors.
                    continue
                if tensor.is_cpu:
                    # use metadata_group for CPU tensors
                    handle = torch.distributed.broadcast(
                        tensor, src=self.ranks[src], group=metadata_group, async_op=True
                    )
                else:
                    # use group for GPU tensors
                    handle = torch.distributed.broadcast(
                        tensor, src=self.ranks[src], group=group, async_op=True
                    )
                async_handles.append(handle)
            for async_handle in async_handles:
                async_handle.wait()

        else:
            metadata_list = self.broadcast_object(None, src=src)
            tensor_dict = {}
            async_handles = []
            for key, value in metadata_list:
                if isinstance(value, TensorMetadata):
                    tensor = torch.empty(
                        value.size, dtype=value.dtype, device=value.device
                    )
                    if tensor.numel() == 0:
                        # Skip broadcasting empty tensors.
                        tensor_dict[key] = tensor
                        continue
                    if tensor.is_cpu:
                        # use metadata_group for CPU tensors
                        handle = torch.distributed.broadcast(
                            tensor,
                            src=self.ranks[src],
                            group=metadata_group,
                            async_op=True,
                        )
                    else:
                        # use group for GPU tensors
                        handle = torch.distributed.broadcast(
                            tensor, src=self.ranks[src], group=group, async_op=True
                        )
                    async_handles.append(handle)
                    tensor_dict[key] = tensor
                else:
                    tensor_dict[key] = value
            for async_handle in async_handles:
                async_handle.wait()
        return tensor_dict

    def send_tensor_dict(
        self,
        tensor_dict: Dict[str, Union[torch.Tensor, Any]],
        dst: Optional[int] = None,
        all_gather_group: Optional["GroupCoordinator"] = None,
        async_send: bool = False,
    ) -> Optional[List[P2PWork]]:
        """Send the input tensor dictionary.
        NOTE: `dst` is the local rank of the source rank.
        """
        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return tensor_dict

        all_gather_size = 1 if all_gather_group is None else all_gather_group.world_size
        all_gather_rank = (
            0 if all_gather_group is None else all_gather_group.rank_in_group
        )

        group = self.device_group
        metadata_group = self.cpu_group

        if dst is None:
            dst = (self.rank_in_group + 1) % self.world_size
        assert dst < self.world_size, f"Invalid dst rank ({dst})"

        assert isinstance(
            tensor_dict, dict
        ), f"Expecting a dictionary, got {type(tensor_dict)}"
        metadata_list, tensor_list = _split_tensor_dict(tensor_dict)
        # Note: While switching to Device-to-Device (D2D) would introduce an extra
        # Device-to-Host (D2H) memory copy overhead for serialization, our benchmarks
        # show better overall transmission performance with D2D due to:
        # 1. Superior D2D transfer bandwidth
        # 2. Ability to overlap send and recv operations
        # Thus the net performance gain justifies this approach.

        send_func = torch.distributed.isend if async_send else torch.distributed.send
        p2p_works = self.send_object(metadata_list, dst=dst, async_send=async_send)

        for tensor in tensor_list:
            if tensor.numel() == 0:
                # Skip sending empty tensors.
                continue

            # send-allgather: send only a slice, then do allgather.
            if all_gather_group is not None and tensor.numel() % all_gather_size == 0:
                tensor = tensor.reshape(all_gather_size, -1)[all_gather_rank]

            comm_group = metadata_group if tensor.is_cpu else group
            work = send_func(tensor, self.ranks[dst], group=comm_group)
            if async_send:
                p2p_works.append(P2PWork(work, tensor))
        return p2p_works

    def recv_tensor_dict(
        self,
        src: Optional[int] = None,
        all_gather_group: Optional["GroupCoordinator"] = None,
    ) -> Optional[Dict[str, Union[torch.Tensor, Any]]]:
        """Recv the input tensor dictionary.
        NOTE: `src` is the local rank of the source rank.
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return None

        all_gather_size = 1 if all_gather_group is None else all_gather_group.world_size
        all_gather_rank = (
            0 if all_gather_group is None else all_gather_group.rank_in_group
        )

        group = self.device_group
        metadata_group = self.cpu_group

        if src is None:
            src = (self.rank_in_group - 1) % self.world_size
        assert src < self.world_size, f"Invalid src rank ({src})"

        recv_metadata_list = self.recv_object(src=src)
        tensor_dict: Dict[str, Any] = {}
        for key, value in recv_metadata_list:
            if isinstance(value, TensorMetadata):
                tensor = torch.empty(value.size, dtype=value.dtype, device=value.device)
                if tensor.numel() == 0:
                    # Skip broadcasting empty tensors.
                    tensor_dict[key] = tensor
                    continue

                # send-allgather: send only a slice, then do allgather.
                use_all_gather = (
                    all_gather_group is not None
                    and tensor.numel() % all_gather_size == 0
                )

                if use_all_gather:
                    orig_shape = tensor.shape
                    tensor = tensor.reshape(all_gather_size, -1)[all_gather_rank]

                # We have to use irecv here to make it work for both isend and send.
                comm_group = metadata_group if tensor.is_cpu else group
                work = torch.distributed.irecv(
                    tensor, src=self.ranks[src], group=comm_group
                )
                work.wait()

                if use_all_gather:
                    tensor = all_gather_group.all_gather(tensor, dim=0)
                    tensor = tensor.reshape(orig_shape)

                tensor_dict[key] = tensor
            else:
                tensor_dict[key] = value
        return tensor_dict

    def barrier(self):
        """Barrier synchronization among the group.
        NOTE: don't use `device_group` here! `barrier` in NCCL is
        terrible because it is internally a broadcast operation with
        secretly created GPU tensors. It is easy to mess up the current
        device. Use the CPU group instead.
        """
        torch.distributed.barrier(group=self.cpu_group)

    def send(self, tensor: torch.Tensor, dst: Optional[int] = None) -> None:
        """Sends a tensor to the destination rank in a non-blocking way"""
        """NOTE: `dst` is the local rank of the destination rank."""
        if dst is None:
            dst = (self.rank_in_group + 1) % self.world_size

        pynccl_comm = self.pynccl_comm
        if pynccl_comm is not None and not pynccl_comm.disabled:
            pynccl_comm.send(tensor, dst)
        else:
            torch.distributed.send(tensor, self.ranks[dst], self.device_group)

    def recv(
        self, size: torch.Size, dtype: torch.dtype, src: Optional[int] = None
    ) -> torch.Tensor:
        """Receives a tensor from the source rank."""
        """NOTE: `src` is the local rank of the source rank."""
        if src is None:
            src = (self.rank_in_group - 1) % self.world_size

        tensor = torch.empty(size, dtype=dtype, device=self.device)
        pynccl_comm = self.pynccl_comm
        if pynccl_comm is not None and not pynccl_comm.disabled:
            pynccl_comm.recv(tensor, src)
        else:
            torch.distributed.recv(tensor, self.ranks[src], self.device_group)
        return tensor

    def destroy(self):
        if self.device_group is not None:
            torch.distributed.destroy_process_group(self.device_group)
            self.device_group = None
        if self.cpu_group is not None:
            torch.distributed.destroy_process_group(self.cpu_group)
            self.cpu_group = None
        if self.pynccl_comm is not None:
            self.pynccl_comm = None
        if self.pymscclpp_comm is not None:
            self.pymscclpp_comm.destroy()
        if self.ca_comm is not None:
            self.ca_comm = None
        if self.mq_broadcaster is not None:
            self.mq_broadcaster = None


_WORLD: Optional[GroupCoordinator] = None


def get_world_group() -> GroupCoordinator:
    assert _WORLD is not None, "world group is not initialized"
    return _WORLD


def init_world_group(
    ranks: List[int], local_rank: int, backend: str, recovered_rank: bool = False
) -> GroupCoordinator:
    return GroupCoordinator(
        group_ranks=[ranks],
        local_rank=local_rank,
        torch_distributed_backend=backend,
        use_pynccl=False,
        use_pymscclpp=False,
        use_custom_allreduce=False,
        use_torch_symm_mem_all_reduce=False,
        use_hpu_communicator=False,
        use_xpu_communicator=False,
        use_npu_communicator=False,
        group_name="world",
        recovered_rank=recovered_rank,
    )


def init_model_parallel_group(
    group_ranks: List[List[int]],
    local_rank: int,
    backend: str,
    use_pynccl: Optional[bool] = None,
    use_custom_allreduce: Optional[bool] = None,
    use_message_queue_broadcaster: bool = False,
    group_name: Optional[str] = None,
    use_mscclpp_allreduce: Optional[bool] = None,
    use_torch_symm_mem_allreduce: Optional[bool] = None,
    recovered_rank: bool = False,
    rank_offset: int = 0,
    max_world_size: Optional[int] = None,
) -> GroupCoordinator:
    if use_custom_allreduce is None:
        use_custom_allreduce = _ENABLE_CUSTOM_ALL_REDUCE
    if use_mscclpp_allreduce is None:
        use_mscclpp_allreduce = _ENABLE_MSCCLPP_ALL_REDUCE
    if use_torch_symm_mem_allreduce is None:
        use_torch_symm_mem_allreduce = _ENABLE_TORCH_SYMM_MEM_ALL_REDUCE
    return GroupCoordinator(
        group_ranks=group_ranks,
        local_rank=local_rank,
        torch_distributed_backend=backend,
        use_pynccl=(
            not (_is_npu or _is_xpu or backend == "mooncake")
            if use_pynccl is None
            else use_pynccl
        ),
        use_pymscclpp=use_mscclpp_allreduce,
        use_custom_allreduce=use_custom_allreduce,
        use_torch_symm_mem_all_reduce=use_torch_symm_mem_allreduce,
        use_hpu_communicator=True,
        use_xpu_communicator=True,
        use_npu_communicator=True,
        use_message_queue_broadcaster=use_message_queue_broadcaster,
        group_name=group_name,
        recovered_rank=recovered_rank,
        rank_offset=rank_offset,
        max_world_size=max_world_size,
    )


_TP: Optional[GroupCoordinator] = None
_ATTN_TP: Optional[GroupCoordinator] = None
_ATTN_CP: Optional[GroupCoordinator] = None
_DCP: Optional[GroupCoordinator] = None

# duplicate GroupCoordinator for prefill in PD-Multiplexing
_PDMUX_PREFILL_TP_GROUP: Optional[GroupCoordinator] = None

_ENABLE_PDMUX_P_TP: bool = False


def set_pdmux_status(enable_prefill_multiplexing: bool):
    global _ENABLE_PDMUX_P_TP
    _ENABLE_PDMUX_P_TP = enable_prefill_multiplexing


def get_tp_group() -> GroupCoordinator:
    if _ENABLE_PDMUX_P_TP:
        assert (
            _PDMUX_PREFILL_TP_GROUP is not None
        ), "tensor model parallel group for PD-Multiplexing Prefill is not initialized"
        return _PDMUX_PREFILL_TP_GROUP
    assert _TP is not None, "tensor model parallel group is not initialized"
    return _TP


def get_attn_tp_group() -> GroupCoordinator:
    assert (
        _ATTN_TP is not None
    ), "attention tensor model parallel group is not initialized"
    return _ATTN_TP


def get_attn_cp_group() -> GroupCoordinator:
    assert (
        _ATTN_CP is not None
    ), "attention context model parallel group is not initialized"
    return _ATTN_CP


def get_dcp_group_no_assert() -> Optional[GroupCoordinator]:
    return _DCP


def get_dcp_group() -> GroupCoordinator:
    assert _DCP is not None, "decode context parallel group is not initialized"
    return _DCP


_MOE_DP: Optional[GroupCoordinator] = None
_MOE_EP: Optional[GroupCoordinator] = None
_MOE_TP: Optional[GroupCoordinator] = None


def get_moe_dp_group() -> GroupCoordinator:
    assert _MOE_DP is not None, "moe data parallel group is not initialized"
    return _MOE_DP


def get_moe_ep_group() -> GroupCoordinator:
    assert _MOE_EP is not None, "expert model parallel group is not initialized"
    return _MOE_EP


def get_moe_tp_group() -> GroupCoordinator:
    assert _MOE_TP is not None, "expert model parallel group is not initialized"
    return _MOE_TP


# kept for backward compatibility
get_tensor_model_parallel_group = get_tp_group

_PP: Optional[GroupCoordinator] = None


def get_pp_group() -> GroupCoordinator:
    assert _PP is not None, "pipeline model parallel group is not initialized"
    return _PP


# kept for backward compatibility
get_pipeline_model_parallel_group = get_pp_group


def get_mooncake_transfer_engine():
    """
    Return the shared MooncakeTransferEngine if initialized in device_communicators,
    else None. Used by disaggregation mooncake backend and mem_cache mooncake_store.
    """
    from sglang.srt.distributed.device_communicators.mooncake_transfer_engine import (
        get_mooncake_transfer_engine as _get_engine,
    )

    return _get_engine()


@contextmanager
def graph_capture(stream=None):
    """
    `graph_capture` is a context manager which should surround the code that
    is capturing the CUDA graph. Its main purpose is to ensure that the
    some operations will be run after the graph is captured, before the graph
    is replayed. It returns a `GraphCaptureContext` object which contains the
    necessary data for the graph capture. Currently, it only contains the
    stream that the graph capture is running on. This stream is set to the
    current CUDA stream when the context manager is entered and reset to the
    default stream when the context manager is exited. This is to ensure that
    the graph capture is running on a separate stream from the default stream,
    in order to explicitly distinguish the kernels to capture
    from other kernels possibly launched on background in the default stream.
    """
    with (
        get_tp_group().graph_capture(stream=stream) as context,
        get_pp_group().graph_capture(context),
    ):
        with contextlib.ExitStack() as stack:
            seen = {id(_TP), id(_PP)}
            for group in (_DCP, _ATTN_TP, _MOE_EP, _MOE_TP):
                if group is not None and id(group) not in seen:
                    seen.add(id(group))
                    stack.enter_context(group.graph_capture(context))
            yield context


logger = logging.getLogger(__name__)

_ENABLE_CUSTOM_ALL_REDUCE = True
_ENABLE_MSCCLPP_ALL_REDUCE = False
_ENABLE_TORCH_SYMM_MEM_ALL_REDUCE = False
# Read once at import: whether CustomAllReduceV2 is opted in on a multi-node
# (MNNVL) group. Used on the all_reduce hot path (see GroupCoordinator).
_CA_V2_MULTINODE = envs.SGLANG_ENABLE_CUSTOM_ALL_REDUCE_V2_MULTINODE.get()


def set_custom_all_reduce(enable: bool):
    global _ENABLE_CUSTOM_ALL_REDUCE
    _ENABLE_CUSTOM_ALL_REDUCE = enable


def set_mscclpp_all_reduce(enable: bool):
    global _ENABLE_MSCCLPP_ALL_REDUCE
    _ENABLE_MSCCLPP_ALL_REDUCE = enable


def set_torch_symm_mem_all_reduce(enable: bool):
    global _ENABLE_TORCH_SYMM_MEM_ALL_REDUCE
    _ENABLE_TORCH_SYMM_MEM_ALL_REDUCE = enable


# TODO: refactor in-tree platforms to get rid of this wrapper
def get_default_distributed_backend(device: str) -> str:
    # We deliberately go through ``platforms.current_platform`` (rather than
    # ``from ... import current_platform``) so each call resolves through the
    # platforms package's lazy ``__getattr__`` and picks up runtime overrides
    # of ``_current_platform`` (e.g. in tests).
    if device == platforms.current_platform.device_type:
        return platforms.current_platform.get_torch_distributed_backend_str()
    return _DEVICE_TO_DISTRIBUTED_BACKEND.get(device, "gloo")


def _create_global_tcp_store(
    rank: int,
    world_size: int,
    dist_init_addr: Optional[str] = None,
    allow_dynamic_membership: bool = False,
) -> None:
    """Create a global TCPStore for coordination across ranks.

    This function creates a TCPStore that all ranks can use for coordination
    (e.g., for NIXL buffer setup).
    """
    from torch.distributed import TCPStore

    base_store_port = envs.SGLANG_TCP_STORE_PORT.get()

    master_ip = os.environ.get("MASTER_ADDR")
    if not master_ip and allow_dynamic_membership and dist_init_addr:
        addr = dist_init_addr
        if addr.startswith("tcp://"):
            addr = addr[len("tcp://") :]
        master_ip = addr.rsplit(":", 1)[0]
    if not master_ip:
        logger.warning(
            "Could not determine master IP for global TCPStore. "
            "Broadcasting from rank 0 to all ranks."
        )
        if rank == 0:
            master_ip = get_local_ip_auto()
            ip_list = [master_ip]
        else:
            ip_list = [None]
        torch.distributed.broadcast_object_list(ip_list, src=0)
        master_ip = ip_list[0]

    try:
        if allow_dynamic_membership:
            is_master = rank == 0
            tcp_store = TCPStore(
                host_name=master_ip,
                port=base_store_port,
                is_master=is_master,
                wait_for_workers=False,
            )
        else:
            is_master = rank == 0
            tcp_store = TCPStore(
                host_name=master_ip,
                port=base_store_port,
                world_size=world_size,
                is_master=is_master,
            )
        set_global_tcp_store(tcp_store)
        logger.info(
            "Created global TCPStore at %s:%d (rank=%d, is_master=%s)",
            master_ip,
            base_store_port,
            rank,
            is_master,
        )
    except Exception as e:
        logger.warning(
            "Failed to create global TCPStore at %s:%d: %s. "
            "Components requiring TCPStore (like NIXL) may not work.",
            master_ip,
            base_store_port,
            e,
        )


def init_distributed_environment(
    world_size: int = -1,
    rank: int = -1,
    distributed_init_method: str = "env://",
    local_rank: int = -1,
    backend: str = "nccl",
    timeout: Optional[int] = None,
    moe_a2a_backend: Optional[str] = None,
    recovered_rank: bool = False,
    max_world_size: Optional[int] = None,
):
    logger.debug(
        "world_size=%d rank=%d local_rank=%d " "distributed_init_method=%s backend=%s",
        world_size,
        rank,
        local_rank,
        distributed_init_method,
        backend,
    )
    if not torch.distributed.is_initialized():
        global _MODEL_PARALLEL_GROUP_TIMEOUT
        assert distributed_init_method is not None, (
            "distributed_init_method must be provided when initializing "
            "distributed environment"
        )
        if timeout is not None:
            assert isinstance(timeout, (int)), "timeout must be a number"
            assert timeout > 0, "timeout must be positive"
            timeout = timedelta(seconds=timeout)

        _MODEL_PARALLEL_GROUP_TIMEOUT = timeout

        if backend == "mooncake":
            from mooncake.pg import MooncakeBackendOptions

            use_max_ws = max_world_size and max_world_size > world_size
            ar_size = max_world_size if use_max_ws else world_size
            active_ranks = torch.zeros(ar_size, dtype=torch.int32, device="cuda")
            active_ranks[:world_size] = 1
            if use_max_ws:
                pg_options = MooncakeBackendOptions(
                    active_ranks, recovered_rank, max_world_size
                )
            else:
                pg_options = MooncakeBackendOptions(active_ranks, recovered_rank)
        else:
            pg_options = get_torch_distributed_pg_options()

        # this backend is used for WORLD
        torch.distributed.init_process_group(
            backend=backend,
            init_method=distributed_init_method,
            world_size=world_size,
            rank=rank,
            timeout=timeout,
            pg_options=pg_options,
        )

        # Create a global TCPStore for coordination (used by NIXL)
        if moe_a2a_backend == "nixl":
            _create_global_tcp_store(
                rank,
                world_size,
                dist_init_addr=distributed_init_method,
                allow_dynamic_membership=(
                    recovered_rank
                    or (max_world_size is not None and max_world_size > world_size)
                ),
            )

    # set the local rank
    # local_rank is not available in torch ProcessGroup,
    # see https://github.com/pytorch/pytorch/issues/122816
    if local_rank == -1:
        # local rank not set, this usually happens in single-node
        # setting, where we can use rank as local rank
        if distributed_init_method == "env://":
            local_rank = int(os.environ.get("LOCAL_RANK", "0"))
        else:
            local_rank = rank
    global _WORLD
    if _WORLD is None:
        ranks = list(range(torch.distributed.get_world_size()))
        _WORLD = init_world_group(
            ranks, local_rank, backend, recovered_rank=recovered_rank
        )
    else:
        assert (
            _WORLD.world_size == torch.distributed.get_world_size()
        ), "world group already initialized with a different world size"


def initialize_model_parallel(
    tensor_model_parallel_size: int = 1,
    expert_model_parallel_size: int = 1,
    pipeline_model_parallel_size: int = 1,
    attention_data_parallel_size: int = 1,
    attention_context_model_parallel_size: int = 1,
    moe_data_model_parallel_size: int = 1,
    decode_context_parallel_size: int = 1,
    backend: Optional[str] = None,
    duplicate_tp_group: bool = False,
    enable_symm_mem: bool = False,
    recovered_rank: bool = False,
    rank_offset: int = 0,
    max_world_size: Optional[int] = None,
) -> None:
    """
    Initialize model parallel groups.

    Arguments:
        tensor_model_parallel_size: number of GPUs used for tensor model
            parallelism.
        expert_model_parallel_size: number of GPUs used for expert model
            parallelism.
        pipeline_model_parallel_size: number of GPUs used for pipeline model
            parallelism.
        attention_data_parallel_size: number of GPUs used for attention data
            parallelism.
        attention_context_model_parallel_size: number of GPUs used for attention context
            parallelism.
        moe_data_model_parallel_size: number of GPUs used for moe data
            parallelism.
        decode_context_parallel_size: number of GPUs used for decode context
            parallelism, which splits the KV cache across GPUs within each
            tensor-parallel group during decoding. Must be a divisor of
            tensor_model_parallel_size and is currently only supported on the
            AMD HIP platform.

    Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
    use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
    the model pipeline. The present function will
    create 4 tensor model-parallel groups and 2 pipeline model-parallel groups:
        4 tensor model-parallel groups:
            [g0, g1], [g2, g3], [g4, g5], [g6, g7]
        2 pipeline model-parallel groups:
            [g0, g2, g4, g6], [g1, g3, g5, g7]

    Let's say we use 2 GPUs for attention context parallelism (attn_cp_size=2) and 4 GPUs for
    attention tensor parallelism (attn_tp_size=4). As for MoE part, we use 2 GPUs for moe data
    parallelism (moe_dp_size=2) and 4 GPUs for moe expert parallelism (moe_ep_size=4). The present
    function will create the following groups:
        2 tensor model-parallel groups:
            [g0, g1, g2, g3], [g4, g5, g6, g7]
        4 attention context-parallel groups:
            [g0, g4], [g1, g5], [g2, g6], [g3, g7]
        2 moe expert-parallel groups:
            [g0, g1, g2, g3], [g4, g5, g6, g7]
        4 moe data-parallel groups:
            [g0, g4], [g1, g5], [g2, g6], [g3, g7]

    Note that for efficiency, the caller should make sure adjacent ranks
    are on the same DGX box. For example if we are using 2 DGX-1 boxes
    with a total of 16 GPUs, rank 0 to 7 belong to the first box and
    ranks 8 to 15 belong to the second box.
    """
    # Get world size and rank. Ensure some consistencies.
    assert torch.distributed.is_initialized()
    backend = backend or torch.distributed.get_backend(get_world_group().device_group)

    # Joiners construct their local TP/PP layout in global rank space.
    world_size: int = (
        tensor_model_parallel_size * pipeline_model_parallel_size
        if recovered_rank
        else torch.distributed.get_world_size()
    )

    if world_size != tensor_model_parallel_size * pipeline_model_parallel_size:
        raise RuntimeError(
            f"world_size ({world_size}) is not equal to "
            f"tensor_model_parallel_size ({tensor_model_parallel_size}) x "
            f"pipeline_model_parallel_size ({pipeline_model_parallel_size})"
        )
    if decode_context_parallel_size < 1:
        raise RuntimeError(
            f"decode_context_parallel_size ({decode_context_parallel_size}) must be >= 1"
        )
    if decode_context_parallel_size > 1 and not (is_hip() or is_cuda()):
        raise RuntimeError(
            "Decode context parallel (decode_context_parallel_size > 1) is "
            "currently only supported on the AMD HIP platform or CUDA platform, but got "
            f"decode_context_parallel_size ({decode_context_parallel_size}) "
            "on a non-HIP or non-CUDA platform."
        )
    if tensor_model_parallel_size % decode_context_parallel_size != 0:
        raise RuntimeError(
            f"tensor_model_parallel_size ({tensor_model_parallel_size}) must be divisible by "
            f"decode_context_parallel_size ({decode_context_parallel_size})"
        )

    # Build the tensor model-parallel groups.
    num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size
    global _TP
    assert _TP is None, "tensor model parallel group is already initialized"
    group_ranks = []
    for tp_group_idx in range(num_tensor_model_parallel_groups):
        ranks = list(
            range(
                tp_group_idx * tensor_model_parallel_size,
                (tp_group_idx + 1) * tensor_model_parallel_size,
            )
        )
        group_ranks.append(ranks)

    # message queue broadcaster is only used in tensor model parallel group
    _TP = init_model_parallel_group(
        group_ranks,
        get_world_group().local_rank,
        backend,
        use_message_queue_broadcaster=envs.SGLANG_USE_MESSAGE_QUEUE_BROADCASTER.get(),
        group_name="tp",
        recovered_rank=recovered_rank,
        rank_offset=rank_offset,
        max_world_size=max_world_size,
    )

    if duplicate_tp_group:
        global _PDMUX_PREFILL_TP_GROUP
        assert (
            _PDMUX_PREFILL_TP_GROUP is None
        ), "tensor model parallel group for PD-Multiplexing Prefill is already initialized"
        _PDMUX_PREFILL_TP_GROUP = init_model_parallel_group(
            group_ranks,
            get_world_group().local_rank,
            backend,
            use_message_queue_broadcaster=envs.SGLANG_USE_MESSAGE_QUEUE_BROADCASTER.get(),
            group_name="pdmux_prefill_tp",
            recovered_rank=recovered_rank,
            rank_offset=rank_offset,
            max_world_size=max_world_size,
        )
        if _TP.pynccl_comm:
            _TP.pynccl_comm.disabled = False
            _PDMUX_PREFILL_TP_GROUP.pynccl_comm.disabled = False

    # Build decode context-parallel groups inside each TP group only when DCP is enabled.
    global _DCP
    assert _DCP is None, "decode context parallel group is already initialized"
    if decode_context_parallel_size > 1:
        dcp_group_ranks = []
        for tp_group in group_ranks:
            for start in range(0, len(tp_group), decode_context_parallel_size):
                dcp_group_ranks.append(
                    tp_group[start : start + decode_context_parallel_size]
                )
        _DCP = init_model_parallel_group(
            dcp_group_ranks,
            get_world_group().local_rank,
            backend,
            use_message_queue_broadcaster=envs.SGLANG_USE_MESSAGE_QUEUE_BROADCASTER.get(),
            group_name="dcp",
            recovered_rank=recovered_rank,
        )
        if get_tensor_model_parallel_rank() == 0:
            logger.info(
                f"DCP enabled, dcp_size={decode_context_parallel_size}, tp_size={tensor_model_parallel_size}"
            )

    attn_dp_size = attention_data_parallel_size
    attn_cp_size = attention_context_model_parallel_size
    attn_tp_size = tensor_model_parallel_size // attn_cp_size // attn_dp_size

    global _ATTN_CP
    assert (
        _ATTN_CP is None
    ), "attention context model parallel group is already initialized"
    if attn_cp_size == tensor_model_parallel_size:
        _ATTN_CP = _TP
    else:
        group_ranks = []
        for tp_group_idx in range(num_tensor_model_parallel_groups):
            for dp_idx in range(attn_dp_size):
                for attn_tp_idx in range(attn_tp_size):
                    st = (
                        tp_group_idx * tensor_model_parallel_size
                        + dp_idx * attn_tp_size * attn_cp_size
                        + attn_tp_idx
                    )
                    en = (
                        tp_group_idx * tensor_model_parallel_size
                        + (dp_idx + 1) * attn_tp_size * attn_cp_size
                        + attn_tp_idx
                    )
                    ranks = list(range(st, en, attn_tp_size))
                    group_ranks.append(ranks)
        _ATTN_CP = init_model_parallel_group(
            group_ranks,
            get_world_group().local_rank,
            backend,
            use_message_queue_broadcaster=envs.SGLANG_USE_MESSAGE_QUEUE_BROADCASTER.get(),
            group_name="attn_cp",
            recovered_rank=recovered_rank,
            rank_offset=rank_offset,
            max_world_size=max_world_size,
        )

    from sglang.srt.layers.sampler import SYNC_TOKEN_IDS_ACROSS_TP

    global _ATTN_TP
    assert (
        _ATTN_TP is None
    ), "attention tensor model parallel group is already initialized"
    if attn_tp_size == tensor_model_parallel_size:
        _ATTN_TP = _TP
    else:
        group_ranks = []
        for tp_group_idx in range(num_tensor_model_parallel_groups):
            for cp_dp_combined_idx in range(attn_cp_size * attn_dp_size):
                st = (
                    tp_group_idx * tensor_model_parallel_size
                    + cp_dp_combined_idx * attn_tp_size
                )
                en = (
                    tp_group_idx * tensor_model_parallel_size
                    + (cp_dp_combined_idx + 1) * attn_tp_size
                )
                ranks = list(range(st, en))
                group_ranks.append(ranks)

        _ATTN_TP = init_model_parallel_group(
            group_ranks,
            get_world_group().local_rank,
            backend,
            use_pynccl=SYNC_TOKEN_IDS_ACROSS_TP or enable_symm_mem,
            use_custom_allreduce=False,
            use_torch_symm_mem_allreduce=False,
            use_message_queue_broadcaster=envs.SGLANG_USE_MESSAGE_QUEUE_BROADCASTER.get(),
            group_name="attention_tp",
            recovered_rank=recovered_rank,
            rank_offset=rank_offset,
            max_world_size=max_world_size,
        )

    moe_ep_size = expert_model_parallel_size
    moe_dp_size = moe_data_model_parallel_size
    moe_tp_size = tensor_model_parallel_size // moe_ep_size // moe_dp_size

    global _MOE_DP
    assert _MOE_DP is None, "moe data parallel group is already initialized"
    if attn_cp_size > moe_dp_size:
        # When moe_dp_size < attn_cp_size, CP ranks must share tokens before MoE.
        # The MOE_DP group includes these CP partners, so the existing DP
        # allgather/scatter handles the token sharing.
        _MOE_DP = _ATTN_CP
    elif moe_dp_size == tensor_model_parallel_size:
        _MOE_DP = _TP
    else:
        group_ranks = []
        for tp_group_idx in range(num_tensor_model_parallel_groups):
            for tp_ep_combined_idx in range(moe_tp_size * moe_ep_size):
                st = tp_group_idx * tensor_model_parallel_size + tp_ep_combined_idx
                en = (
                    tp_group_idx + 1
                ) * tensor_model_parallel_size + tp_ep_combined_idx
                ranks = list(range(st, en, moe_tp_size * moe_ep_size))
                group_ranks.append(ranks)
        _MOE_DP = init_model_parallel_group(
            group_ranks,
            get_world_group().local_rank,
            backend,
            group_name="moe_dp",
            recovered_rank=recovered_rank,
            rank_offset=rank_offset,
            max_world_size=max_world_size,
        )

    global _MOE_EP
    assert _MOE_EP is None, "expert model parallel group is already initialized"
    # NPU requires a standalone group for MOE expert parallelism
    if moe_ep_size == tensor_model_parallel_size and not _is_npu:
        _MOE_EP = _TP
    else:
        group_ranks = []
        for tp_group_idx in range(num_tensor_model_parallel_groups):
            for moe_dp_idx in range(moe_dp_size):
                for moe_tp_idx in range(moe_tp_size):
                    st = (
                        tp_group_idx * tensor_model_parallel_size
                        + moe_dp_idx * moe_ep_size * moe_tp_size
                        + moe_tp_idx
                    )
                    en = st + moe_ep_size * moe_tp_size
                    ranks = list(range(st, en, moe_tp_size))
                    group_ranks.append(ranks)
        _MOE_EP = init_model_parallel_group(
            group_ranks,
            get_world_group().local_rank,
            backend,
            use_pynccl=False,
            use_custom_allreduce=False,
            group_name="moe_ep",
            recovered_rank=recovered_rank,
            rank_offset=rank_offset,
            max_world_size=max_world_size,
        )

    global _MOE_TP
    assert _MOE_TP is None, "expert model parallel group is already initialized"
    if moe_tp_size == tensor_model_parallel_size:
        _MOE_TP = _TP
    else:
        group_ranks = []
        for tp_group_idx in range(num_tensor_model_parallel_groups):
            for ep_dp_combined_idx in range(moe_ep_size * moe_dp_size):
                st = (
                    tp_group_idx * tensor_model_parallel_size
                    + ep_dp_combined_idx * moe_tp_size
                )
                en = (
                    tp_group_idx * tensor_model_parallel_size
                    + (ep_dp_combined_idx + 1) * moe_tp_size
                )
                ranks = list(range(st, en))
                group_ranks.append(ranks)
        _MOE_TP = init_model_parallel_group(
            group_ranks,
            get_world_group().local_rank,
            backend,
            use_pynccl=False,
            use_custom_allreduce=False,
            group_name="moe_tp",
            recovered_rank=recovered_rank,
            rank_offset=rank_offset,
            max_world_size=max_world_size,
        )

    # Build the pipeline model-parallel groups.
    num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
    global _PP
    assert _PP is None, "pipeline model parallel group is already initialized"
    group_ranks = []
    for pp_group_idx in range(num_pipeline_model_parallel_groups):
        ranks = list(
            range(pp_group_idx, world_size, num_pipeline_model_parallel_groups)
        )
        group_ranks.append(ranks)
    # pipeline parallel does not need custom allreduce
    _PP = init_model_parallel_group(
        group_ranks,
        get_world_group().local_rank,
        backend,
        use_custom_allreduce=False,
        group_name="pp",
        recovered_rank=recovered_rank,
        rank_offset=rank_offset,
        max_world_size=max_world_size,
    )


def create_custom_parallel_group(
    group_ranks: List[int], backend: str = "gloo"
) -> Optional[torch.distributed.ProcessGroup]:
    """
    Create a custom parallel group based on the provided ranks.

    Args:
        group_ranks: The list of ranks that the CURRENT process wants to join.
                     (e.g., Rank 0 passes [0...7], Rank 8 passes [8...15])
        backend: The communication backend (default: "gloo").

    Returns:
        The ProcessGroup if the current rank is in group_ranks, else None.
    """
    assert torch.distributed.is_initialized()

    world_size = torch.distributed.get_world_size()
    rank = torch.distributed.get_rank()

    local_config = sorted(list(set(group_ranks)))
    gathered_configs = [None for _ in range(world_size)]

    torch.distributed.all_gather_object(gathered_configs, local_config)

    unique_groups = []
    seen_signatures = set()

    for config in gathered_configs:
        config_tuple = tuple(config)
        if config_tuple not in seen_signatures:
            seen_signatures.add(config_tuple)
            unique_groups.append(list(config_tuple))

    unique_groups.sort(key=lambda x: x[0])

    my_new_group = None

    for g_ranks in unique_groups:
        group = torch.distributed.new_group(ranks=g_ranks, backend=backend)

        if set(g_ranks) == set(local_config):
            my_new_group = group
            logger.debug(
                f"Rank {rank} successfully created/joined custom group: {g_ranks}"
            )

    return my_new_group


def ensure_model_parallel_initialized(
    tensor_model_parallel_size: int,
    expert_model_parallel_size: int,
    pipeline_model_parallel_size: int,
    decode_context_parallel_size: int = 1,
    backend: Optional[str] = None,
) -> None:
    """Helper to initialize model parallel groups if they are not initialized,
    or ensure tensor-parallel and pipeline-parallel sizes are equal to expected
    values if the model parallel groups are initialized.
    """
    backend = backend or torch.distributed.get_backend(get_world_group().device_group)
    if not model_parallel_is_initialized():
        initialize_model_parallel(
            tensor_model_parallel_size=tensor_model_parallel_size,
            expert_model_parallel_size=expert_model_parallel_size,
            pipeline_model_parallel_size=pipeline_model_parallel_size,
            decode_context_parallel_size=decode_context_parallel_size,
            backend=backend,
        )
        return

    assert get_tensor_model_parallel_world_size() == tensor_model_parallel_size, (
        "tensor parallel group already initialized, but of unexpected size: "
        f"{get_tensor_model_parallel_world_size()=} vs. "
        f"{tensor_model_parallel_size=}"
    )
    pp_world_size = get_pp_group().world_size
    assert pp_world_size == pipeline_model_parallel_size, (
        "pipeline parallel group already initialized, but of unexpected size: "
        f"{pp_world_size=} vs. "
        f"{pipeline_model_parallel_size=}"
    )
    if decode_context_parallel_size > 1:
        dcp_world_size = get_dcp_group().world_size
        assert (
            dcp_world_size == decode_context_parallel_size
        ), f"decode context parallel group already initialized, but of unexpected size: {dcp_world_size=} {decode_context_parallel_size=}"


def model_parallel_is_initialized():
    """Check if tensor and pipeline parallel groups are initialized."""
    return _TP is not None and _PP is not None


_TP_STATE_PATCHED = False


@contextmanager
def patch_tensor_parallel_group(tp_group: GroupCoordinator):
    """Patch the tp group temporarily until this function ends.

    This method is for draft workers of speculative decoding to run draft model
    with different tp degree from that of target model workers.

    Args:
        tp_group (GroupCoordinator): the tp group coordinator
    """
    global _TP_STATE_PATCHED
    assert not _TP_STATE_PATCHED, "Should not call when it's already patched"

    _TP_STATE_PATCHED = True
    old_tp_group = get_tp_group()
    global _TP
    _TP = tp_group
    try:
        yield
    finally:
        # restore the original state
        _TP_STATE_PATCHED = False
        _TP = old_tp_group


def get_world_size():
    """Return world size for the world group."""
    return get_world_group().world_size


def get_world_rank():
    """Return my rank for the world group."""
    return get_world_group().rank_in_group


def get_tensor_model_parallel_world_size():
    """Return world size for the tensor model parallel group."""
    return get_tp_group().world_size


def get_dcp_world_size():
    return get_dcp_group().world_size


def get_dcp_rank():
    return get_dcp_group().rank_in_group


def get_tensor_model_parallel_rank():
    """Return my rank for the tensor model parallel group."""
    return get_tp_group().rank_in_group


# ATTN_TP
def get_attn_tensor_model_parallel_world_size():
    """Return world size for the attention tensor model parallel group."""
    return get_attn_tp_group().world_size


def get_attn_tensor_model_parallel_rank():
    """Return my rank for the attention tensor model parallel group."""
    return get_attn_tp_group().rank_in_group


# ATTN_CP
def get_attn_context_model_parallel_world_size():
    """Return world size for the attention context model parallel group."""
    return get_attn_cp_group().world_size


def get_attn_context_model_parallel_rank():
    """Return my rank for the attention context model parallel group."""
    return get_attn_cp_group().rank_in_group


def get_pipeline_model_parallel_world_size():
    """Return world size for the pipeline model parallel group."""
    return get_pp_group().world_size


def get_pipeline_model_parallel_rank():
    """Return my rank for the pipeline model parallel group."""
    return get_pp_group().rank_in_group


# MOE_DP
def get_moe_data_parallel_world_size():
    """Return world size for the moe data parallel group."""
    return get_moe_dp_group().world_size


def get_moe_data_parallel_rank():
    """Return my rank for the moe data parallel group."""
    return get_moe_dp_group().rank_in_group


# MOE_EP
def get_moe_expert_parallel_world_size():
    """Return world size for the moe expert parallel group."""
    return get_moe_ep_group().world_size


def get_moe_expert_parallel_rank():
    """Return my rank for the moe expert parallel group."""
    return get_moe_ep_group().rank_in_group


# MOE_TP
def get_moe_tensor_parallel_world_size():
    """Return world size for the moe tensor parallel group."""
    return get_moe_tp_group().world_size


def get_moe_tensor_parallel_rank():
    """Return my rank for the moe tensor parallel group."""
    return get_moe_tp_group().rank_in_group


def destroy_model_parallel():
    """Set the groups to none and destroy them."""
    dwdp_mgr = get_global_dwdp_manager()
    if dwdp_mgr is not None:
        dwdp_mgr.cleanup()
        set_global_dwdp_manager(None)

    global _TP
    if _TP:
        _TP.destroy()
    _TP = None

    global _PP
    if _PP:
        _PP.destroy()
    _PP = None

    global _DCP
    if _DCP:
        _DCP.destroy()
    _DCP = None

    global _MOE_EP
    if _MOE_EP:
        _MOE_EP.destroy()
    _MOE_EP = None

    global _MOE_TP
    if _MOE_TP:
        _MOE_TP.destroy()
    _MOE_TP = None

    global _ATTN_CP
    global _MOE_DP
    # Destroy _MOE_DP before _ATTN_CP since it may alias _ATTN_CP.
    # Only destroy if not aliasing another group.
    if _MOE_DP and _MOE_DP is not _ATTN_CP and _MOE_DP is not _TP:
        _MOE_DP.destroy()
    _MOE_DP = None
    if _ATTN_CP:
        _ATTN_CP.destroy()
    _ATTN_CP = None

    global _ATTN_TP
    if _ATTN_TP:
        _ATTN_TP.destroy()
    _ATTN_TP = None

    global _PDMUX_PREFILL_TP_GROUP
    if _PDMUX_PREFILL_TP_GROUP:  # type: ignore[union-attr]
        _PDMUX_PREFILL_TP_GROUP.destroy()
    _PDMUX_PREFILL_TP_GROUP = None


def destroy_distributed_environment():
    global _WORLD, _MODEL_PARALLEL_GROUP_TIMEOUT
    if _WORLD:
        _WORLD.destroy()
    _WORLD = None
    _MODEL_PARALLEL_GROUP_TIMEOUT = None
    if torch.distributed.is_initialized():
        torch.distributed.destroy_process_group()


def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
    destroy_model_parallel()
    destroy_distributed_environment()
    with contextlib.suppress(AssertionError):
        torch.distributed.destroy_process_group()
    if shutdown_ray:
        import ray  # Lazy import Ray

        ray.shutdown()
    gc.collect()
    if not _is_cpu:
        if hasattr(torch, "cuda") and torch.cuda.is_available():
            torch.cuda.empty_cache()
            if hasattr(torch._C, "_host_emptyCache"):
                torch._C._host_emptyCache()
            else:
                logger.warning(
                    "torch._C._host_emptyCache() only available in Pytorch >=2.5"
                )
        elif hasattr(torch, "xpu") and torch.xpu.is_available():
            torch.xpu.empty_cache()
        elif hasattr(torch, "npu") and torch.npu.is_available():
            torch.npu.empty_cache()
        elif hasattr(torch, "musa") and torch.musa.is_available():
            torch.musa.empty_cache()


def in_the_same_node_as(pg: ProcessGroup, source_rank: int = 0) -> List[bool]:
    """
    This is a collective operation that returns if each rank is in the same node
    as the source rank. It tests if processes are attached to the same
    memory system (shared access to shared memory).
    """
    assert (
        torch.distributed.get_backend(pg) != torch.distributed.Backend.NCCL
    ), "in_the_same_node_as should be tested with a non-NCCL group."
    # local rank inside the group
    rank = torch.distributed.get_rank(group=pg)
    world_size = torch.distributed.get_world_size(group=pg)

    # local tensor in each process to store the result
    is_in_the_same_node = torch.tensor(
        [0] * world_size, dtype=torch.int32, device="cpu"
    )

    # global ranks of the processes in the group
    ranks = torch.distributed.get_process_group_ranks(pg)

    magic_message = b"magic_message"
    shm = None

    try:
        with contextlib.suppress(OSError):
            if rank == source_rank:
                # create a shared memory segment
                shm = shared_memory.SharedMemory(
                    create=True, size=128, name=make_shm_name("nodecheck")
                )
                shm.buf[: len(magic_message)] = magic_message
                torch.distributed.broadcast_object_list(
                    [shm.name], src=ranks[source_rank], group=pg
                )
                is_in_the_same_node[rank] = 1
            else:
                # try to open the shared memory segment
                recv = [None]
                torch.distributed.broadcast_object_list(
                    recv, src=ranks[source_rank], group=pg
                )
                name = recv[0]
                # fix to https://stackoverflow.com/q/62748654/9191338
                # Python incorrectly tracks shared memory even if it is not
                # created by the process. The following patch is a workaround.
                with patch(
                    "multiprocessing.resource_tracker.register",
                    lambda *args, **kwargs: None,
                ):
                    shm = shared_memory.SharedMemory(name=name)
                if shm.buf[: len(magic_message)] == magic_message:
                    is_in_the_same_node[rank] = 1
    except Exception as e:
        logger.error("Error ignored in is_in_the_same_node: %s", e)
    finally:
        if shm:
            shm.close()

    torch.distributed.barrier(group=pg)

    # clean up the shared memory segment
    with contextlib.suppress(OSError):
        if rank == source_rank and shm:
            shm.unlink()
    torch.distributed.all_reduce(is_in_the_same_node, group=pg)

    return [x == 1 for x in is_in_the_same_node.tolist()]


vllm_get_pp_group = None
vllm_get_tp_group = None
vllm_get_world_group = None


def monkey_patch_vllm_parallel_state(reverse: bool = False):
    try:
        import vllm.distributed.parallel_state as vllm_parallel_state
    except ImportError:
        return

    global vllm_get_pp_group, vllm_get_tp_group, vllm_get_world_group
    if vllm_get_pp_group is None:
        vllm_get_pp_group = vllm_parallel_state.get_pp_group
        vllm_get_tp_group = vllm_parallel_state.get_tp_group
        vllm_get_world_group = vllm_parallel_state.get_world_group
    if reverse:
        setattr(vllm_parallel_state, "get_pp_group", vllm_get_pp_group)
        setattr(vllm_parallel_state, "get_tp_group", vllm_get_tp_group)
        setattr(vllm_parallel_state, "get_world_group", vllm_get_world_group)
    else:
        setattr(vllm_parallel_state, "get_pp_group", get_pp_group)
        setattr(vllm_parallel_state, "get_tp_group", get_tp_group)
        setattr(vllm_parallel_state, "get_world_group", get_world_group)
